Data collection enables a person or organization to answer relevant questions, evaluate outcomes and make predictions about future probabilities and trends

Accurate data collection is essential to maintaining the integrity of research, making informed business decisions and ensuring quality assurance.

It’s extremeley important that we first assure that the data we are collecting is of high quality, and second that we are using the data to drive ALL business decisions.

A simulation is the imitation of the operation of a real-world process or system over time.

Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time.

• The AFRS Call Centre has been working nonstop since it’s opening at the beginning of the pandemic.

• Since the beginning there has been a steady increase of calls per month, and we expect that trend to continue. Reference Liz Moakes Demand and Funding Position Statement for specifics.

• Even through this increase the Call Centre has performed consistently overtime in managing this call overload.

• This simulation will look at the effect of increasing the total number of operators in the Call Centre at any given time, but specifically during peak times 20:00-24:00.

First our model picks a number from a poisson distribution based off the hourly distributions. A poisson distribution tracks the expected number of times something will help in a given amount of time. (i.e. how many calls will a call centre receive in an hour)

Next the model uses that number to generate n random numbers in the time span of that hour, so if the number generated was 4, then four random times between midnight and 1 a.m. would be generated, they could be close together or far apart. This simulates a semi-realistic call centre, since it assumes that each person calling is independent of another person calling.

• DES Works as a pipeline, the same way an interaction in the call centre might occur.

• A caller calls, and is met with either a wait time or an operator. A strict rule of 60 seconds was set for callers to abandon. (This is stricter than realistic)

• If an operator is available they will answer the call, the length of the call is determined from a distribution of the average of the average monthly call lengths plus an additional wrap-up time.

• After that length of time has passed the operator will once again become available for another call.

• This simulation runs for 133 days with n operators per day

• Considers professional referrals, which then are followed-up after two hours. These follow-ups take priority over incoming calls.

• Does not include downtime for supervision, etc. so results are UNDERESTIMATES

## # A tibble: 2 x 3
##   Abandoned count Percent_of_Total
##   <chr>     <int>            <dbl>
## 1 FALSE     15607             0.95
## 2 TRUE        899             0.05

• It IS possible to reach the goals on our KPIs.
• We were able to have a 5% abandon rate, and all calls that were answered were served within 60 seconds.
• We need more funding to have 8-10 operators and 1-2 professional line operators working during peak times.
• The call centre is operating at low capacity with the amount of staff on hand, therefore we need more funding to reach our goals/KPIs.

• The utilization is between 55-65%, this is to account for one the stress breaks that might befall call operators dealing with mental health patients, supervision, and the downtime that is the reality of a call centre, especially in the early hours of the morning. Remember this is the AVERAGE utilization for ALL hours. The peak average utilization is likely ~85-90%

• “This simulation doesn’t reflect reality”, well in reality, it does. It was built off of the real distribution of the data, so although it has not actually happened, this is what is MOST LIKELY to happen.

If there any questions please feel free to contact:

Hansel Palencia - Senior Information Analyst - Informatics